Three-dimensional Mapping with Augmented Navigation Cost through Deep Learning

This work addresses the problem of mapping terrain features based on inertial and LiDAR measurements to estimate the navigation cost for an autonomous ground robot. Unlike most indoor applications, where surfaces are usually human-made, flat, and structured, external environments may be unpredictable regarding the types and conditions of the travel surfaces, such as traction characteristics and inclination. Attaining full autonomy in outdoor environments requires a mobile ground robot to perform the fundamental localization and mapping tasks in unfamiliar environments, but with the added challenge of unknown terrain conditions. A fuller representation of the environment is undamental to increase confidence and to reduce navigation costs. To this end, we propose a methodology composed of five main steps: i) speed-invariant inertial transformation; ii) roughness level classification; iii) navigation cost estimation; iv) sensor fusion through Deep Learning; and v) estimation of navigation costs for untraveled regions. To validate the methodology, we carried out experiments using ground robots in different outdoor environments with different terrain characteristics. Results show that the terrain maps thus obtained are a faithful representation of outdoor environments allowing for accurate and reliable path planning.